A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

Abstract:

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

• Periodical: Advanced Materials Research (Volumes 605-607)
• Pages: 2442-2446
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.605-607.2442
• Online since: December 2012
• Authors: Xin Ran Li, Yan Xia Jin
• Keywords: Chaotic Sequence, Mutation, Premature, Quantum-Behaved Particle Swarm Optimization, Tent Mapping

References

[1] Kennedy J，Eberhart R C. Particle swarm optimization[J]. Institute of Electrical and Electronics Engineers, 1995(11): 1942-(1948).

[2] Duan Yuhong，Gao Yuelin，Li Jimin. A new adaptive particle swarm optimization algorithm with dynamically changing inertia weight[J]. Intelligent Information Management Systems and Tech-nologies, 2006, 2(2): 245-255.

[3] Gao Yuelin，Duan Yuhong. An adaptive particle swarm optimization algorithm with new random inertia weight[J]. Communications in Computer and Information Science，2007(3)：342-350.

DOI: 10.1007/978-3-540-74282-1_39

[4] Zhang C S, Sun J G. An alternate two phases particle swarm optimization algorithm for flow shop scheduling problem [J]. Expert Systems with Applications, 2009, 36(3): 5162-5167.

DOI: 10.1016/j.eswa.2008.06.036

[5] Jiang Y, Hu T S, Huang C C, et al. An improved particle swarm optimization algorithm[J]. Applied Mathematics and Computation, 2007, 193(1): 231-239.

DOI: 10.1016/j.amc.2007.03.047

[6] Sun J，Feng B，Xu W B. Particle swarm optimization with particles having quantum behavior[C]. Proceedings of 2004 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2004: 325-331.

DOI: 10.1109/cec.2004.1330875

[7] Shan L, Qiang H, Li J, et al. A Chaos Optimization Algorithm Based on Tent Mapping[J]. Control and Decision. 2005, 20(2): 179 -182.

[8] Liu Junfang, Gao Yuelin. A Quantum Particle Swarm Optimization Algorithm with Adaptive Mutation[J]. Computer Engineering and Applications, 2011, 47(3): 41-43.